Top Cloud Platforms: features, pricing models, and best-fit scenarios for leading cloud service providers

Distributed systems and distributed computing are not just trends; they are the norm in IT now. We all know cloud services, and each major IT giant has developed its cloud platform. The real challenge now is choosing the best solution for your business from the many options available.

In this article, you'll find an overview of the most popular cloud platforms in 2024. With this analysis, you’ll be equipped to make an informed decision about which platform best suits your needs.

The platforms are presented in a top-down list of website traffic, from Amazon Web Services (AWS), which has over 65 million visitors per month, to Microsoft Azure, which has over 7 million visitors per month. We do not favor any cloud platform, but we can assist you in selecting the best technology for your specific requirements.

Amazon Web Services (AWS)

https://aws.amazon.com/

Amazon Web Services (AWS) was launched in 2006 with the initial goal of becoming a cloud services provider, offering companies access to scalable computing resources over the Internet on a pay-as-you-go basis.

The Features of Amazon Web Services (AWS)

Compute Resources: AWS offers a wide range of virtual machines through EC2 with various instance types, including high-performance options like C5 (CPU), P3 (GPU), and R5 (memory). Lambda enables serverless code execution, perfect for event-driven and microservice architectures. Elastic Beanstalk provides an easy-to-use platform for deploying web apps supporting popular programming languages (Java, .NET, Python, etc.).

Data Storage: Amazon S3 is an object storage service with virtually unlimited capacity, versioning, encryption, and lifecycle management. EBS offers high-performance block storage ideal for databases and I/O-intensive apps. Glacier is designed for cost-effective archival storage.

Databases: RDS supports relational databases (MySQL, PostgreSQL, SQL Server, MariaDB, Oracle) with automatic backup and scaling. DynamoDB is a fully managed NoSQL database built for high-performance and large-scale data handling. Aurora is compatible with MySQL and PostgreSQL.

Analytics and Big Data: Redshift is a scalable data warehouse for complex queries and high performance. Kinesis and Firehose enable real-time data streaming. Athena allows SQL queries on S3 data, simplifying big data analysis without infrastructure setup.

AI and Machine Learning: SageMaker is a platform for building and deploying machine learning models, supporting pre-built algorithms and custom data. Rekognition analyzes images and videos for face and object recognition. Comprehend processes text to extract insights and relationships between words.

Amazon Web Services (AWS) Pricing Models

• Pay-as-you-go: The standard model where customers pay only for the actual use of computing, storage, and other services.

• Reserved Instances: Discounts for long-term contracts (1 or 3 years) with commitment to specific instance types.

• Spot Instances: Cost-effective instances that can be terminated by AWS as needed, ideal for flexible or backup tasks.

• Free Tier: A program for new users offering limited access to many services for 12 months.

Amazon Web Services (AWS) Use Cases

Startups and Enterprises: Provides a powerful, flexible infrastructure suitable for projects of any size and complexity. Its scalability makes it ideal for rapidly growing startups and large global enterprises.

Data Analytics and Big Data: Offers a comprehensive suite of tools for data analysis, making it an excellent choice for businesses needing complex analytics and big data processing.

Application Development and Deployment: Supports CI/CD automation and streamlined app deployment, simplifying development and infrastructure management.

Google Cloud Platform (GCP)

https://cloud.google.com/

The Google Cloud Platform platform was launched in 2008. At the start of its work, the platform provided distributed computing resources that supported Google's infrastructure, including tools for working with big data and artificial intelligence tools. The platform's main task was declared to be support for systems with global availability.

The Features of Google Cloud Platform (GCP)

Compute Resources: GCP offers a Computing Engine for virtual machines, Cloud Functions for serverless computing, and an App Engine for app deployment. Kubernetes Engine facilitates container management and microservice deployment.

Data Storage: Cloud Storage provides highly available object storage with seamless integration with other services. Persistent Disk delivers high-performance block storage. Bigtable is a scalable NoSQL database ideal for IoT data processing and analytics.

Databases: Cloud SQL supports relational databases such as PostgreSQL, MySQL, and SQL Server. Cloud Spanner is a global relational database ensuring high availability and consistency. Firestore enables real-time data storage and synchronization.

Analytics and Big Data: BigQuery is a fully managed data warehouse supporting SQL queries and capable of analyzing petabytes of data in seconds. Dataflow supports real-time data streaming and ETL processes.

AI and Machine Learning: GCP integrates TensorFlow, a popular open-source ML library. Vertex AI assists in building, training, and deploying ML models. AutoML enables model creation without deep ML expertise.

Development and Integration: GCP supports CI/CD pipelines through Cloud Build and Cloud Run for container deployment.

Google Cloud Platform (GCP) Pricing Models:

• Pay-as-you-go: Payment based on actual resource usage.

• Committed Use Contracts: Discounts for long-term commitments.

• Sustained Use Discounts: Automatic discounts for prolonged use.

• Free Tier: Free resources for specific services.

Google Cloud Platform (GCP) Use Cases

Data Analytics and Big Data: BigQuery and other tools make GCP an ideal choice for businesses prioritizing large-scale data processing.

Python Development and Machine Learning: TensorFlow and other GCP-optimized tools attract Python and ML developers.

Containerized Application Development: Kubernetes Engine and Cloud Run simplify container deployment and management, making GCP perfect for microservices.

Oracle Cloud Infrastructure (OCI)

https://www.oracle.com/cloud/

The Oracle Cloud Infrastructure platform was launched in 2016. It was created to solve the problems of Oracle corporate clients, offering powerful cloud services for databases, business applications, and hybrid infrastructure. The main goal of the platform was to support the operation of high-performance systems, as well as the ability to migrate corporate solutions to the Oracle cloud.

The Features of Oracle Cloud Infrastructure (OCI)

Compute Resources: Oracle Cloud offers high-performance virtual machines with extensive customization options. Compute Instances are scalable based on application needs. OCI also provides Bare Metal Instances for applications requiring maximum compute power without virtualization.

Data Storage: Oracle Cloud Storage includes Object Storage for unstructured data, Block Volumes for block storage, and File Storage for shared file access. Oracle Autonomous Data Warehouse and Oracle Exadata Cloud Service offer optimized solutions for database workloads.

Databases: Oracle Cloud is renowned for its advanced database solutions. Oracle Autonomous Database is a fully managed cloud database that automates setup, patching, and security. OCI also supports MySQL, PostgreSQL, and NoSQL databases, offering flexibility for open-source users.

Analytics and Big Data: Oracle Analytics Cloud provides powerful tools for data visualization, reporting, and real-time analytics. Big Data Service and Data Flow support big data processing and integration for complex analytical tasks.

AI and Machine Learning: Oracle AI includes tools for automated machine learning, data processing, and predictive modeling. Oracle Machine Learning (OML) runs directly in Oracle databases, ensuring low-latency analytics.

Security and Compliance: Oracle Cloud prioritizes security with data encryption, access management, and threat protection. OCI is also certified for numerous international standards and regulations.

Oracle Cloud Infrastructure (OCI) Pricing Models

• Pay-as-you-go: Pay for resource usage, allowing flexible cost scaling.

• Reserved Instances: Long-term contracts offering discounts on cloud services.

• Universal Credits: Payment model that lets users pay as they go and switch between services.

• Free Tier: Free access to basic services for testing and experimentation.

Oracle Cloud Infrastructure (OCI) Use Cases

Traditional Databases and Migration: OCI is ideal for organizations using Oracle databases that want to move their data to the cloud with minimal cost.

Analytics and Data Science: OCI offers powerful tools for big data analysis and processing, making it an excellent choice for businesses requiring robust data analytics.

Mission-Critical Applications: Known for high security and reliability, Oracle Cloud is well-suited for applications in finance, healthcare, and the public sector.

IBM Cloud

https://www.ibm.com/cloud

The current version of IBM Cloud has been around since 2017, though earlier versions of the platform were introduced by IBM as IBM SmartCloud in 2011. The platform is designed for companies looking for hybrid cloud solutions, with a strong focus on artificial intelligence, data processing, and security.

The Features of IBM Cloud

Compute Resources: IBM Cloud offers customizable virtual servers with various CPU and memory configurations. Container management services, such as IBM Cloud Kubernetes Service, enable container deployment and management using Kubernetes. IBM Cloud Functions support serverless computing, ideal for event-driven code execution without infrastructure management.

Data Storage: IBM Cloud Object Storage is a scalable, secure solution for large data storage and backups. IBM Block Storage and IBM File Storage provide high-performance file storage. IBM Cloud Databases offer managed database services, including PostgreSQL, MySQL, MongoDB, and Redis.

Databases: IBM Cloud Databases include managed solutions for relational databases (PostgreSQL, MySQL, etc.) and NoSQL options (MongoDB, Redis). Db2 on Cloud offers a hybrid cloud solution for relational data, ensuring scalability and reliability.

Analytics and Big Data: IBM Watson Studio and IBM DataStage provide tools for data analytics, integration, and machine learning model creation. IBM Cloud Pak for Data is an integrated platform for data processing and analysis with AI and ML support.

AI and Machine Learning: IBM Watson is a leading AI platform with solutions for natural language processing, speech recognition, computer vision, and process automation. Watson Machine Learning supports model development, training, and deployment using popular frameworks like TensorFlow and PyTorch.

Hybrid Cloud: IBM Cloud supports hybrid cloud solutions, including integration with Red Hat OpenShift, allowing applications to be deployed in both on-premises and cloud environments.

IBM Cloud Pricing Models:

• Pay-as-you-go: Clients pay for actual resource usage, including computing power, storage, and databases.

• Reserved Instances: Long-term contracts for virtual servers offering discounts.

• Subscription: Fixed payment for a set amount of resources over a specified period.

• Free Tier: Limited free access to select services for platform testing.

IBM Cloud Use Cases

Hybrid and Multi-Cloud Solutions: Ideal for organizations seeking to integrate on-premises and cloud solutions, supporting hybrid strategies. AI and Machine Learning: IBM Watson is well-suited for companies needing powerful AI and ML tools for data analysis, process automation, and intelligent application development. Financial and Regulated Industries: Focused on security and compliance, IBM Cloud is a strong choice for financial institutions and organizations with high data protection requirements.

Microsoft Azure

https://azure.microsoft.com/

The Microsoft Azure platform was launched in 2010. Azure is designed as a cloud platform to support hybrid solutions, their integration with Microsoft product ecosystem, and create scalable enterprise applications.

The Features of Microsoft Azure

Compute Resources: Azure offers virtual machines with various configurations, including powerful GPU-enabled VMs for data processing and AI, as well as Azure Functions for serverless computing. App Services provides built-in features for deploying and managing web apps, with auto-scaling and security support.

Storage: Azure Blob Storage offers scalable, reliable object storage for different data types. Azure Disk Storage and Azure File Storage provide high-speed file storage. Azure Data Lake Storage is optimized for storing and analyzing large volumes of unstructured data.

Databases: Azure SQL Database is a managed relational database with automatic scaling and backup features. Cosmos DB is a globally distributed NoSQL database ideal for data-intensive, low-latency applications. Azure Database for PostgreSQL and MySQL offers fully managed open-source database solutions.

Analytics and Big Data: Azure Synapse Analytics integrates analytical and transactional workflows. Azure Data Factory simplifies building and managing ETL (extract, transform, load) processes.

AI and Machine Learning: Azure Machine Learning provides tools for building, training, and deploying ML models. Cognitive Services offers pre-built APIs for speech recognition, image and text processing. Azure Bot Service supports AI-powered chatbot development.

Integration with Microsoft Products: Azure seamlessly integrates with Microsoft 365, Windows Server, and Active Directory, making it ideal for organizations using Microsoft solutions.

Microsoft Azure Pricing Models:

• Pay-as-you-go: Pay for actual resource usage.

• Reserved Instances: Discounts for long-term use of virtual machines (1 or 3 years).

• Spot VMs: Cost-effective VMs for non-critical tasks that don’t require continuous uptime.

• Free Tier: Limited access to select services for new users.

Microsoft Azure Use Cases Companies Using Microsoft Products: Ideal for organizations leveraging Windows Server, Active Directory, and Microsoft 365, offering seamless integration and compatibility. Hybrid Solutions: Azure Stack enables hybrid cloud infrastructure, combining on-premises and cloud environments, suitable for organizations seeking flexible cloud solutions. Development and DevOps: Tools like Visual Studio and Azure DevOps streamline app development and deployment automation, supporting developers and DevOps teams.

Now that you're known with the features of the most popular cloud platforms, here are some final recommendations:

• Amazon Web Services (AWS): Ideal for deploying distributed systems and compute-intensive workloads that require scalability and innovation.

• Microsoft Azure: The best case for companies that already heavily use Microsoft products, offering simple integration and maximizing the efficiency of your existing software ecosystem.

• Google Cloud Platform (GCP): Good for you, if you need big data processing and machine learning.

• IBM Cloud: A good choice for companies looking to integrate AI and blockchain solutions into their business processes.

• Oracle Cloud Infrastructure (OCI): If you are already using Oracle databases, as well as for distributed systems requiring high computational performance.

When selecting a cloud platform, ask yourself: is cost the only factor that matters? Consider the unique strengths of each platform to find the best fit for your business needs.